Learning to Generate Tips from Song Reviews. (April 2023)

• Record Type: Journal Article
• Authors: Zang, Jingya; Gao, Cuiyun; Chen, Yupan; Xu, Ruifeng; Zhou, Lanjun; Wang, Xuan
• Is Part Of: Neural networks. Volume 161(2023)
• Page Start: 746
• Page End: 756
• Publication Date: 2023-04
• Subjects: Tip generation -- Review ranking -- Pre-trained model -- Song reviews -- Data annotation; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2023.01.049
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Reviews of songs play an important role in online music service platforms. Prior research shows that users can make quicker and more informed decisions when presented with meaningful song reviews. However, reviews of songs are generally long in length and most of them are non-informative for users. It is difficult for users to efficiently grasp meaningful messages for making decisions. To solve this problem, one practical strategy is to provide tips, i.e., short, concise, empathetic, and self-contained descriptions about songs. Tips are produced from song reviews and should express non-trivial insights about the songs. To the best of our knowledge, no prior studies have explored the tip generation task in music domain. In this paper, we create a dataset named MTips for the task and propose a learning-to-generate framework named GenTMS for automatically generating tips from song reviews. The dataset involves 8, 003 Chinese tips/non-tips from 128 songs which are distributed in five different song genres. Experimental results show that GenTMS achieves top-10 precision at 85.56%, outperforming the baseline models by at least 3.34%. Besides, to simulate the practical usage of our proposed framework, we also experiment with previously-unseen songs, during which GenTMS also achieves the best performance with top-10 precision at 78.89% on average. The results demonstrate the effectiveness of the proposed framework in tip generation of the music domain.